import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


A						=			{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    List[Any]:
		assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
		assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
		if args.mlm:
				assert os.path.isfile(args.token_counts							)
				assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
		else:
				assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

		assert args.teacher_type == args.student_type or (
		    args.student_type == "distilbert" and args.teacher_type == "bert"
		)
		assert os.path.isfile(args.student_config							)
		if args.student_pretrained_weights is not None:
				assert os.path.isfile(args.student_pretrained_weights							)

		if args.freeze_token_type_embds:
				assert args.student_type in ["roberta"]

		assert args.alpha_ce >= 0.0
		assert args.alpha_mlm >= 0.0
		assert args.alpha_clm >= 0.0
		assert args.alpha_mse >= 0.0
		assert args.alpha_cos >= 0.0
		assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Any							)   ->    List[str]:
		if args.student_type == "roberta":
				_lowerCamelCase				   =  False
		elif args.student_type == "gpt2":
				_lowerCamelCase				   =  False


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				List[str]							)   ->    int:
		if args.student_type == "roberta":
				_lowerCamelCase				   =  False


def 	lowerCamelCase					(	)   ->    Any:
		_lowerCamelCase				   =  argparse.ArgumentParser(description='Training'							)
		parser.add_argument('--force'			,       action='store_true'			,       help='Overwrite dump_path if it already exists.'							)

		parser.add_argument(
		    '--dump_path'			,       type=UpperCamelCase			,       required=UpperCamelCase			,       help='The output directory (log, checkpoints, parameters, etc.)'							)
		parser.add_argument(
		    '--data_file'			,       type=UpperCamelCase			,       required=UpperCamelCase			,       help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'			,       )

		parser.add_argument(
		    '--student_type'			,       type=UpperCamelCase			,       choices=['distilbert', 'roberta', 'gpt2']			,       required=UpperCamelCase			,       help='The student type (DistilBERT, RoBERTa).'			,       )
		parser.add_argument('--student_config'			,       type=UpperCamelCase			,       required=UpperCamelCase			,       help='Path to the student configuration.'							)
		parser.add_argument(
		    '--student_pretrained_weights'			,       default=UpperCamelCase			,       type=UpperCamelCase			,       help='Load student initialization checkpoint.'							)

		parser.add_argument(
		    '--teacher_type'			,       choices=['bert', 'roberta', 'gpt2']			,       required=UpperCamelCase			,       help='Teacher type (BERT, RoBERTa).'							)
		parser.add_argument('--teacher_name'			,       type=UpperCamelCase			,       required=UpperCamelCase			,       help='The teacher model.'							)

		parser.add_argument('--temperature'			,       default=2.0			,       type=UpperCamelCase			,       help='Temperature for the softmax temperature.'							)
		parser.add_argument(
		    '--alpha_ce'			,       default=0.5			,       type=UpperCamelCase			,       help='Linear weight for the distillation loss. Must be >=0.'							)
		parser.add_argument(
		    '--alpha_mlm'			,       default=0.0			,       type=UpperCamelCase			,       help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'			,       )
		parser.add_argument('--alpha_clm'			,       default=0.5			,       type=UpperCamelCase			,       help='Linear weight for the CLM loss. Must be >=0.'							)
		parser.add_argument('--alpha_mse'			,       default=0.0			,       type=UpperCamelCase			,       help='Linear weight of the MSE loss. Must be >=0.'							)
		parser.add_argument(
		    '--alpha_cos'			,       default=0.0			,       type=UpperCamelCase			,       help='Linear weight of the cosine embedding loss. Must be >=0.'							)

		parser.add_argument(
		    '--mlm'			,       action='store_true'			,       help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'							)
		parser.add_argument(
		    '--mlm_mask_prop'			,       default=0.15			,       type=UpperCamelCase			,       help='Proportion of tokens for which we need to make a prediction.'			,       )
		parser.add_argument('--word_mask'			,       default=0.8			,       type=UpperCamelCase			,       help='Proportion of tokens to mask out.'							)
		parser.add_argument('--word_keep'			,       default=0.1			,       type=UpperCamelCase			,       help='Proportion of tokens to keep.'							)
		parser.add_argument('--word_rand'			,       default=0.1			,       type=UpperCamelCase			,       help='Proportion of tokens to randomly replace.'							)
		parser.add_argument(
		    '--mlm_smoothing'			,       default=0.7			,       type=UpperCamelCase			,       help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'			,       )
		parser.add_argument('--token_counts'			,       type=UpperCamelCase			,       help='The token counts in the data_file for MLM.'							)

		parser.add_argument(
		    '--restrict_ce_to_mask'			,       action='store_true'			,       help='If true, compute the distillation loss only the [MLM] prediction distribution.'			,       )
		parser.add_argument(
		    '--freeze_pos_embs'			,       action='store_true'			,       help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'			,       )
		parser.add_argument(
		    '--freeze_token_type_embds'			,       action='store_true'			,       help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'			,       )

		parser.add_argument('--n_epoch'			,       type=UpperCamelCase			,       default=3			,       help='Number of pass on the whole dataset.'							)
		parser.add_argument('--batch_size'			,       type=UpperCamelCase			,       default=5			,       help='Batch size (for each process).'							)
		parser.add_argument(
		    '--group_by_size'			,       action='store_false'			,       help='If true, group sequences that have similar length into the same batch. Default is true.'			,       )

		parser.add_argument(
		    '--gradient_accumulation_steps'			,       type=UpperCamelCase			,       default=50			,       help='Gradient accumulation for larger training batches.'			,       )
		parser.add_argument('--warmup_prop'			,       default=0.05			,       type=UpperCamelCase			,       help='Linear warmup proportion.'							)
		parser.add_argument('--weight_decay'			,       default=0.0			,       type=UpperCamelCase			,       help='Weight decay if we apply some.'							)
		parser.add_argument('--learning_rate'			,       default=5e-4			,       type=UpperCamelCase			,       help='The initial learning rate for Adam.'							)
		parser.add_argument('--adam_epsilon'			,       default=1e-6			,       type=UpperCamelCase			,       help='Epsilon for Adam optimizer.'							)
		parser.add_argument('--max_grad_norm'			,       default=5.0			,       type=UpperCamelCase			,       help='Max gradient norm.'							)
		parser.add_argument('--initializer_range'			,       default=0.02			,       type=UpperCamelCase			,       help='Random initialization range.'							)

		parser.add_argument(
		    '--fp16'			,       action='store_true'			,       help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'			,       )
		parser.add_argument(
		    '--fp16_opt_level'			,       type=UpperCamelCase			,       default='O1'			,       help=(
		        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
		        'See details at https://nvidia.github.io/apex/amp.html'
		    )			,       )
		parser.add_argument('--n_gpu'			,       type=UpperCamelCase			,       default=1			,       help='Number of GPUs in the node.'							)
		parser.add_argument('--local_rank'			,       type=UpperCamelCase			,       default=-1			,       help='Distributed training - Local rank'							)
		parser.add_argument('--seed'			,       type=UpperCamelCase			,       default=56			,       help='Random seed'							)

		parser.add_argument('--log_interval'			,       type=UpperCamelCase			,       default=5_00			,       help='Tensorboard logging interval.'							)
		parser.add_argument('--checkpoint_interval'			,       type=UpperCamelCase			,       default=40_00			,       help='Checkpoint interval.'							)
		_lowerCamelCase				   =  parser.parse_args()
		sanity_checks(UpperCamelCase							)

		# ARGS #
		init_gpu_params(UpperCamelCase							)
		set_seed(UpperCamelCase							)
		if args.is_master:
				if os.path.exists(args.dump_path							):
						if not args.force:
								raise ValueError(
								    F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
								    ' itUse `--force` if you want to overwrite it'							)
						else:
								shutil.rmtree(args.dump_path							)

				if not os.path.exists(args.dump_path							):
						os.makedirs(args.dump_path							)
				logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""							)

				# SAVE PARAMS #
				logger.info(F"""Param: {args}"""							)
				with open(os.path.join(args.dump_path			,       'parameters.json'							)			,       'w'							) as f:
						json.dump(vars(UpperCamelCase							)			,       UpperCamelCase			,       indent=4							)
				git_log(args.dump_path							)

		_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  MODEL_CLASSES[args.student_type]
		_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		_lowerCamelCase				   =  teacher_tokenizer_class.from_pretrained(args.teacher_name							)
		_lowerCamelCase				   =  {}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				_lowerCamelCase				   =  tokenizer.all_special_tokens.index(UpperCamelCase							)
				_lowerCamelCase				   =  tokenizer.all_special_ids[idx]
		logger.info(F"""Special tokens {special_tok_ids}"""							)
		_lowerCamelCase				   =  special_tok_ids
		_lowerCamelCase				   =  tokenizer.max_model_input_sizes[args.teacher_name]

		# DATA LOADER #
		logger.info(F"""Loading data from {args.data_file}"""							)
		with open(args.data_file			,       'rb'							) as fp:
				_lowerCamelCase				   =  pickle.load(UpperCamelCase							)

		if args.mlm:
				logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""							)
				with open(args.token_counts			,       'rb'							) as fp:
						_lowerCamelCase				   =  pickle.load(UpperCamelCase							)

				_lowerCamelCase				   =  np.maximum(UpperCamelCase			,       1							) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						_lowerCamelCase				   =  0.0  # do not predict special tokens
				_lowerCamelCase				   =  torch.from_numpy(UpperCamelCase							)
		else:
				_lowerCamelCase				   =  None

		_lowerCamelCase				   =  LmSeqsDataset(params=UpperCamelCase			,       data=UpperCamelCase							)
		logger.info('Data loader created.'							)

		# STUDENT #
		logger.info(F"""Loading student config from {args.student_config}"""							)
		_lowerCamelCase				   =  student_config_class.from_pretrained(args.student_config							)
		_lowerCamelCase				   =  True

		if args.student_pretrained_weights is not None:
				logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""							)
				_lowerCamelCase				   =  student_model_class.from_pretrained(args.student_pretrained_weights			,       config=UpperCamelCase							)
		else:
				_lowerCamelCase				   =  student_model_class(UpperCamelCase							)

		if args.n_gpu > 0:
				student.to(F"""cuda:{args.local_rank}"""							)
		logger.info('Student loaded.'							)

		# TEACHER #
		_lowerCamelCase				   =  teacher_model_class.from_pretrained(args.teacher_name			,       output_hidden_states=UpperCamelCase							)
		if args.n_gpu > 0:
				teacher.to(F"""cuda:{args.local_rank}"""							)
		logger.info(F"""Teacher loaded from {args.teacher_name}."""							)

		# FREEZING #
		if args.freeze_pos_embs:
				freeze_pos_embeddings(UpperCamelCase			,       UpperCamelCase							)
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(UpperCamelCase			,       UpperCamelCase							)

		# SANITY CHECKS #
		assert student.config.vocab_size == teacher.config.vocab_size
		assert student.config.hidden_size == teacher.config.hidden_size
		assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
		if args.mlm:
				assert token_probs.size(0							) == stu_architecture_config.vocab_size

		# DISTILLER #
		torch.cuda.empty_cache()
		_lowerCamelCase				   =  Distiller(
		    params=UpperCamelCase			,       dataset=UpperCamelCase			,       token_probs=UpperCamelCase			,       student=UpperCamelCase			,       teacher=UpperCamelCase							)
		distiller.train()
		logger.info('Let\'s go get some drinks.'							)


if __name__ == "__main__":
				main()
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A						=			input('Enter image url: ').strip()
				print(F'''Downloading image from {url} ...''')
				A						=			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A						=			soup.find('meta', {'property': 'og:image'})['content']
				A						=			requests.get(image_url).content
				A						=			F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(F'''Done. Image saved to disk as {file_name}.''')